GCBC: Analyzing chromatin dynamics
25 May 2022
1 Pre-processing
1.1 Load packages
library(Seurat)
library(SeuratWrappers)
library(Signac)
library(harmony)
library(tidyverse)
library(pheatmap)
library(reshape2)
library(ggpubr)
library(plyr)
library(UCell)
library(qlcMatrix)
library(GOfuncR)
library(Homo.sapiens)
library(biomaRt)
library(viridis)
library(grid)
library(writexl)
set.seed(333)1.2 Parameters
cell_type = "GCBC"
cols_gcbc <- c(
"DZ G2M phase" = "#6F66A3",
"DZ-early S phase" = "#006FC4",
"DZ-late S phase" = "#004D89",
"DZ-non-proliferative" = "#356200",
"DZ-LZ transition" = "#D4F0A3",
"LZ" = "#FFF59A",
"LZ-DZ-re-entry commitment" = "#E0EFEF",
"LZ-DZ-transition" = "#4EB2FF",
"LZ-proliferative" = "#9AD3FF",
"MBC" = "#E68098",
"prePC" = "#FECFC7")
cels_order <- c("DZ-non-proliferative","DZ-LZ transition","LZ",
"LZ-DZ-re-entry commitment","LZ-proliferative",
"LZ-DZ-transition","DZ-early S phase",
"DZ-late S phase","DZ G2M phase","MBC",
"prePC")
# Paths
path_to_obj <- paste0(
here::here("scATAC-seq/results/R_objects/level_4/"),
cell_type,
"/",
cell_type,
"_chromVar_CISBP_level_4.rds",
sep = ""
)
path_to_obj_RNA <- paste0(
here::here("scRNA-seq/3-clustering/4-level_4/"),
cell_type,
"/",
cell_type,
"_seu_obj_level_5_eta.rds",
sep = ""
)
path_to_obj_RNA_DE <- paste0(
here::here("scRNA-seq/3-clustering/4-level_4/"),
cell_type,
"/markers_re_entry.rds",
sep = "")
path_to_obj_regulons <- paste0(
here::here("scRNA-seq/3-clustering/4-level_4/"),
cell_type,
"/",
cell_type,
"_seu_obj_level_5_beta_3P_withpyscenic.rds",
sep = ""
)
figures_folder <-"/Users/pauli/Desktop/PAPER_FIGURES/GCBC/2.scATAC/"
path_to_regulons <- here::here("scATAC-seq/results/Pyscenic/list_of_regs.rds")
path_to_mat <- here::here("scRNA-seq/results/all_targets_KFKB_mean_expression_per_cluster_eta_noMBC.rds")1.3 Functions
DARS <- function(ident.1,ident.2){
DARs <- FindMarkers(
ident.1 = ident.1,
ident.2 = ident.2,
object = seurat,
min.pct = 0.05,
test.use = 'LR',
latent.vars = 'nCount_peaks_redefined')
DARs_filtered <- DARs[DARs$p_val_adj < 0.05,]
return(DARs_filtered)}
DARS_matrix <- function(DARs, group){
avgexpr_mat <- AverageExpression(
features = row.names(DARs),
seurat,
assays = "peaks_redefined",
return.seurat = F,
group.by = group,
slot = "data")
return(avgexpr_mat)}
DARS_filtered_max <- function(mat, quant, cluster_interest){
matrix_coefficient <- rowMax(mat) / rowSums(mat)
regions <- which(matrix_coefficient > quantile(matrix_coefficient,
quant))
filtered_regions <- mat[regions,]
filtered_regions_interest_clusters <- filtered_regions[which(apply(X = filtered_regions,
MARGIN = 1,FUN = which.max) %in% cluster_interest),]
print(nrow(filtered_regions_interest_clusters))
return(filtered_regions_interest_clusters)}
pheatmap_plot <- function(matrices,n_cutree_rows,
annotation_col,mycolors_list){
colfunc <- colorRampPalette(c("#4575B4", "white","darkgreen"))
pheatmap(matrices,
scale = "row",
annotation_col = annotation_col,
annotation_colors = mycolors_list,
color = colfunc(100),
angle_col = 45,
show_rownames=F,
border_color = "white",
cluster_rows = T,
cluster_cols = F,
fontsize_col = 10,
cutree_rows = n_cutree_rows,
clustering_distance_cols = "euclidean",
clustering_method = "ward.D2")}2 Load data
2.1 Load scATAC-seq data
seurat <- readRDS(path_to_obj)
seurat## An object of class Seurat
## 144021 features across 21447 samples within 2 assays
## Active assay: peaks_redefined (142257 features, 142254 variable features)
## 1 other assay present: chromvar
## 3 dimensional reductions calculated: umap, lsi, harmonyseurat$level_5 <- revalue(seurat$level_5,
c("DZ/LZ"="DZ-LZ transition",
"DZ_Sphase_HistoneHigh"="DZ-late S phase",
"DZ_G2M"="DZ G2M phase",
"DZ-nonproliferative"="DZ-non-proliferative",
"DZ_Sphase"="DZ-early S phase",
"LZ"="LZ",
"LZ-DZ-re-entry"="LZ-DZ-re-entry commitment",
"LZ-proliferative_BCL2A1neg"="LZ-DZ-transition",
"LZ-proliferative_BCL2A1pos"="LZ-proliferative",
"MBC-like"="MBC",
"PC-precursors"="prePC"))
seurat$level_5 <- factor(seurat$level_5, levels = names(cols_gcbc))
p1 <- DimPlot(seurat,
group.by = "level_5",
cols = cols_gcbc,
pt.size = 2, raster = T) + NoLegend()
#pdf(paste(figures_folder, "Panel_E.pdf", sep=""),width=4,height=4,paper='special')
p1 
2.2 Load scRNA-seq data
seurat_RNA <- readRDS(path_to_obj_RNA)
seurat_RNA## An object of class Seurat
## 37378 features across 72174 samples within 1 assay
## Active assay: RNA (37378 features, 0 variable features)
## 3 dimensional reductions calculated: pca, harmony, umap2.3 Load regulons data
seurat_regulons <- readRDS(path_to_obj_regulons)
seurat_regulons## An object of class Seurat
## 37592 features across 61724 samples within 2 assays
## Active assay: pySCENIC (214 features, 0 variable features)
## 1 other assay present: RNA
## 3 dimensional reductions calculated: pca, harmony, umap2.3.1 Merging re-entry subtypes
seurat$re_entry_merged <- revalue(seurat$level_5,
c("DZ-LZ transition"="DZ-LZ transition",
"DZ-late S phase"="DZ-late S phase",
"DZ G2M phase"="DZ G2M phase",
"DZ-non-proliferative"="DZ-non-proliferative",
"DZ-early S phase"="DZ-early S phase",
"LZ"="LZ",
"LZ-DZ-re-entry commitment"="LZ-DZ-re-entry commitment",
"LZ-DZ-transition"="LZ-DZ-re-entry commitment",
"LZ-proliferative"="LZ-DZ-re-entry commitment",
"MBC"="MBC",
"prePC"="prePC"))
seurat_RNA$merged <- revalue(seurat_RNA$names_level_5_clusters_eta,
c("DZ_LZ transition"="DZ-LZ transition",
"DZ cell cycle exit"="DZ-non-proliferative",
"DZ late Sphase"="DZ-late S phase",
"G2M early Sphase"="DZ G2M phase",
"G2M late Sphase"="DZ G2M phase",
"DZ non proliferative"="DZ-non-proliferative",
"DZ early Sphase"="DZ-early S phase",
"LZ"="LZ",
"LZ_DZ reentry commitment"="LZ-DZ-re-entry commitment",
"LZ_DZ transition"="LZ-DZ-transition",
"LZ proliferative"="LZ-proliferative",
"Precursor MBC"="MBC",
"Reactivated proliferative MBCs"="MBC",
"prePC"="prePC"))
seurat_regulons$merged <- revalue(seurat_regulons$names_level_5_clusters_beta,
c("DZ-cell cycle exit"="DZ-non-proliferative",
"DZ-nonproliferative"="DZ-non-proliferative",
"DZ-nonproliferative_FOXP1hi"="DZ-non-proliferative",
"DZ/LZ"="DZ-LZ transition",
"LZ"="LZ",
"LZ-BCL2A1 neg"="LZ",
"LZ-DZ-re-entry early commitment"="LZ-DZ-re-entry commitment",
"PC-precursors"="prePC",
"MBC-like_proli1"="MBC",
"MBC-like_proli2"="MBC",
"MBC-like_proli3"="MBC",
"MBC-like_nonproli"="MBC",
"MBC-like_FCRL4+"="MBC",
"LZ-proliferative_BCL2A1pos"="LZ-proliferative",
"LZ-proliferative_BCL2A1neg"="LZ-DZ-transition",
"DZ_Sphase_HistoneHigh"="DZ-late S phase",
"DZ_Sphase"="DZ-early S phase",
"DZ_G2M_CCNBHigh"="DZ G2M phase",
"DZ_G2M_HistoneHigh"="DZ G2M phase"))3 From the DZ to the LZ
A pairwise differential accessibility analysis between the major biological identities revealed that the DZ-LZ transition was seamless, with not major chromatin changes.
Idents(seurat) <- seurat$re_entry_merged
step1 <- DARS(ident.1 = "DZ-non-proliferative",
ident.2 = "DZ-LZ transition")
step2 <- DARS(ident.1 = "DZ-LZ transition",
ident.2 = "LZ")step1_mat <- DARS_matrix(DARs = step1,
group = "re_entry_merged")
step2_mat <- DARS_matrix(DARs = step2,
group = "re_entry_merged")
path_matrix <- rbind(step1_mat$peaks_redefined,
step2_mat$peaks_redefined)3.1 Selecting top candidates regions
path_matrix <- rbind(step1_mat$peaks_redefined,
step2_mat$peaks_redefined)
cluster_interest = c(4,5,6)
top_candidates1 <- DARS_filtered_max(path_matrix, 0,
cluster_interest)## [1] 3858top_candidates1_selection <- DARS_filtered_max(top_candidates1[,cluster_interest],
0.6, c(1,2,3))## [1] 15433.2 Visualization of top candidates regions in not merged context
step1_not_merged_mat <- DARS_matrix(step1,
group ="level_5")
step2_not_merged_mat <- DARS_matrix(step2,
group ="level_5")
path_not_merged_matrix <- rbind(step1_not_merged_mat$peaks_redefined,step2_not_merged_mat$peaks_redefined)
cell_types <- names(table(seurat$level_5))
annotation_col = data.frame(
cell_type = cell_types)
rownames(annotation_col) <- cell_types
mycolors <- cols_gcbc
names(mycolors) <- cell_types
mycolors_list <- list(mycolors)
names(mycolors_list) <- "cell_type"
#pdf(paste0(figures_folder, "heatmap_DARs_DZtoLZ.pdf"),width=8,height=6,paper='special')
out_DZ_LZ = pheatmap_plot(path_not_merged_matrix[row.names(top_candidates1_selection),cels_order],
n_cutree_rows = 3,
annotation_col,
mycolors_list)
3.2.1 Definition of the sub-clusters
cutree_out = cutree(out_DZ_LZ$tree_row, k=3)
cutree_df <- as.data.frame(cbind(names(cutree_out),cutree_out))
cluster1 <- cutree_df[cutree_df$cutree_out == 1,]
cluster2 <- cutree_df[cutree_df$cutree_out == 2,]
cluster3 <- cutree_df[cutree_df$cutree_out == 3,]
nrow(cluster1)## [1] 668nrow(cluster2)## [1] 851nrow(cluster3)## [1] 244 From the LZ to return to the DZ
A pairwise differential accessibility analysis between the major biological identities revealed an extensive chromatin modulation along the canonical pathway.
Idents(seurat) <- seurat$re_entry_merged
step1_re_entry_merged <- DARS(ident.1 = "LZ",
ident.2 = "LZ-DZ-re-entry commitment")
step2_re_entry_merged <- DARS(ident.1 = "LZ-DZ-re-entry commitment",
ident.2 = "DZ-early S phase")step1_re_entry_merged_mat <- DARS_matrix(step1_re_entry_merged,
group ="re_entry_merged")
step2_re_entry_merged_mat <- DARS_matrix(step2_re_entry_merged,
group ="re_entry_merged")
path_reentry_merged_matrix <- rbind(step1_re_entry_merged_mat$peaks_redefined,
step2_re_entry_merged_mat$peaks_redefined)4.1 Selecting top candidates regions
cluster_interest = c(6,7,2)
top_candidates_reentry <- DARS_filtered_max(path_reentry_merged_matrix, 0, cluster_interest)## [1] 2653top_candidates_reentry_selection <- DARS_filtered_max(top_candidates_reentry[,cluster_interest],0.6, c(1,2,3))## [1] 10614.2 Visualization of top candidates regions in not merged context
step1_re_entry_not_merged_mat <- DARS_matrix(step1_re_entry_merged,
group ="level_5")
step2_re_entry_not_merged_mat <- DARS_matrix(step2_re_entry_merged,
group ="level_5")
path_reentry_not_merged_matrix <- rbind(step1_re_entry_not_merged_mat$peaks_redefined,step2_re_entry_not_merged_mat$peaks_redefined)
DZ_LZ_heatmap <- pheatmap_plot(path_reentry_not_merged_matrix[row.names(top_candidates_reentry_selection),cels_order],
n_cutree_rows = 4,
annotation_col,
mycolors_list)
#pdf(paste0(figures_folder, "heatmap_DARs_LZtoDZ.pdf"),width=8,height=6,paper='special')
DZ_LZ_heatmap
# Definition of the sub-clusters
cutree_DZ_LZ_heatmap_non_merged = cutree(DZ_LZ_heatmap$tree_row,
k=4)
cutree_df <- as.data.frame(cbind(names(cutree_DZ_LZ_heatmap_non_merged),cutree_DZ_LZ_heatmap_non_merged))
cluster1_DZ_LZ <- cutree_df[cutree_df$cutree_DZ_LZ_heatmap_non_merged == 1,]
cluster2_DZ_LZ <- cutree_df[cutree_df$cutree_DZ_LZ_heatmap_non_merged == 2,]
cluster3_DZ_LZ <- cutree_df[cutree_df$cutree_DZ_LZ_heatmap_non_merged == 3,]
cluster4_DZ_LZ <- cutree_df[cutree_df$cutree_DZ_LZ_heatmap_non_merged == 4,]4.2.1 Signature of the LZ cluster
print(paste0("Number of regions:", nrow(cluster2_DZ_LZ)))## [1] "Number of regions:208"pheatmap_plot(path_reentry_not_merged_matrix[unlist(cluster2_DZ_LZ$V1),cels_order], n_cutree_rows = 1, annotation_col, mycolors_list)
seurat <- AddModuleScore(
seurat,
name = 'LZ-module',
features = list(cluster2_DZ_LZ$V1))
colfunc <- colorRampPalette(c("#4575B4", "white","darkgreen"))
#pdf(paste(figures_folder, "Panel_G1.pdf", sep=""),width=5,height=5,paper='special')
FeaturePlot(seurat,
order = TRUE,
min.cutoff = 'q1',
max.cutoff = 'q99',
pt.size = 2,
features = "LZ.module1",
raster = T) +
theme(plot.title = element_blank()) +
scale_color_gradientn( colours = c('lightgrey', 'darkgreen')) + NoLegend()
4.2.1.1 Motif enrichment analysis cluster LZ
enriched.motifs <- FindMotifs(
object = seurat,
features = cluster2_DZ_LZ$V1
)
write_xlsx(enriched.motifs,here::here("scATAC-seq/results/tables/supplementary_table_GCBC_MEA_re_entryI.xlsx"))
DT::datatable(enriched.motifs)4.2.2 Signature cluster re-entry I
print(paste0("Number of regions:", nrow(cluster3_DZ_LZ)))## [1] "Number of regions:293"pheatmap_plot(path_reentry_not_merged_matrix[unlist(cluster3_DZ_LZ$V1),cels_order],
n_cutree_rows = 1,
annotation_col,
mycolors_list)
seurat <- AddModuleScore(
seurat,
name = 'reentry-module',
features = list(cluster3_DZ_LZ$V1))
colfunc <- colorRampPalette(c("#4575B4", "white","darkgreen"))
#pdf(paste(figures_folder, "Panel_G2.pdf", sep=""),width=5,height=5,paper='special')
FeaturePlot(seurat,
order = TRUE,
min.cutoff = 'q1',
max.cutoff = 'q99',
pt.size = 2,
features = "reentry.module1",
raster = T) +
theme(plot.title = element_blank()) +
scale_color_gradientn( colours = c('lightgrey', 'darkgreen')) + NoLegend()
4.2.2.1 Motif enrichment re-entry I
enriched.motifs <- FindMotifs(
object = seurat,
features = cluster3_DZ_LZ$V1
)
write_xlsx(enriched.motifs,here::here("scATAC-seq/results/tables/supplementary_table_GCBC_MEA_re_entryII.xlsx"))
DT::datatable(enriched.motifs)4.2.3 Signature re-entry II
print(paste0("Number of regions:", nrow(cluster1_DZ_LZ)))## [1] "Number of regions:467"pheatmap_plot(path_reentry_not_merged_matrix[unlist(cluster1_DZ_LZ$V1),cels_order],
n_cutree_rows = 1,
annotation_col,
mycolors_list)
seurat <- AddModuleScore(
seurat,
name = 'reentryII-module',
features = list(cluster1_DZ_LZ$V1))
colfunc <- colorRampPalette(c("#4575B4", "white","darkgreen"))
#pdf(paste(figures_folder, "Panel_G3.pdf", sep=""),width=5,height=5,paper='special')
FeaturePlot(seurat,
order = TRUE,
min.cutoff = 'q1',
max.cutoff = 'q99',
pt.size = 2,
features = "reentryII.module1",
raster = T) +
theme(plot.title = element_blank()) +
scale_color_gradientn( colours = c('lightgrey', 'darkgreen')) + NoLegend()
4.2.3.1 Motif enrichment analysis cluster 2
enriched.motifs <- FindMotifs(
object = seurat,
features = cluster1_DZ_LZ$V1
)
write_xlsx(enriched.motifs,here::here("scATAC-seq/results/tables/supplementary_table_GCBC_MEA_re_entryIII.xlsx"))
DT::datatable(enriched.motifs)5 Studying target transcription factors
5.1 BATF family
p1 <- FeaturePlot(seurat,
order = TRUE,
features = "ENSG00000156127-LINE436-BATF-D-N1",
min.cutoff = 'q1',
max.cutoff = 'q99',
pt.size = 0.4) +
theme(plot.title = element_blank())
p2 <- FeaturePlot(seurat_RNA,
order = TRUE,
features = "BATF",
min.cutoff = 'q1',
max.cutoff = 'q99',
pt.size = 0.4) +
theme(plot.title = element_blank())
p1 | p2
seurat[["level_5"]] <- factor(unlist(seurat[["level_5"]]), levels= cels_order)
seurat_RNA[["merged"]] <- factor(unlist(seurat_RNA[["merged"]]), levels= cels_order)
seurat_regulons[["merged"]] <- factor(unlist(seurat_regulons[["merged"]]), levels= cels_order)
p1 <- VlnPlot(seurat,
features = c("ENSG00000156127-LINE435-BATF-D-N1"),
group.by = "level_5", pt.size = 0) + NoLegend() + xlab("") + ylab("") +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_text(size = rel(1), angle = 0),
axis.text.y = element_text(size = rel(1)))
p2 <- VlnPlot(seurat_RNA,
features = c("BATF"),
group.by = "merged", pt.size = 0) + NoLegend() + xlab("") + ylab("") +
theme(legend.position = "none",
# axis.text.x = element_blank(),
# axis.ticks.x = element_blank(),
axis.title.y = element_text(size = rel(1), angle = 0),
axis.text.y = element_text(size = rel(1)))
#pdf(paste(figures_folder, "Violin_module_final_r-entry.pdf", sep=""),width=4,height=8,paper='special')
#patchwork::wrap_plots(plotlist = list(p1,p2), ncol = 1)
p1
p2
5.2 NFkB family
p1 <- FeaturePlot(seurat,
order = TRUE,
features = "ENSG00000109320-LINE3203-NFKB1-D-N1",
min.cutoff = 'q1',
max.cutoff = 'q99',
pt.size = 0.4) +
theme(plot.title = element_blank())
p2 <- FeaturePlot(seurat_RNA,
order = TRUE,
features = "NFKB1",
min.cutoff = 'q1',
max.cutoff = 'q99',
pt.size = 0.4) +
theme(plot.title = element_blank())
p1 | p2
FeaturePlot(seurat,
order = TRUE,
features = c("ENSG00000162924-LINE3217-REL-D-N3",
"ENSG00000173039-LINE3227-RELA-D-N13",
"ENSG00000104856-LINE3201-RELB-D",
"ENSG00000109320-LINE3203-NFKB1-D-N1",
"ENSG00000077150-LINE3189-NFKB2-D-N1"),
min.cutoff = 'q1',
max.cutoff = 'q99',
pt.size = 0.4,
ncol = 2) +
theme(plot.title = element_blank()) 
seurat[["level_5"]] <- factor(unlist(seurat[["level_5"]]), levels= cels_order)
seurat_RNA[["merged"]] <- factor(unlist(seurat_RNA[["merged"]]), levels= cels_order)
seurat_regulons[["merged"]] <- factor(unlist(seurat_regulons[["merged"]]), levels= cels_order)
p1 <- VlnPlot(seurat,
features = c("ENSG00000109320-LINE3203-NFKB1-D-N1"),
group.by = "level_5", pt.size = 0) + NoLegend() + xlab("") + ylab("") +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_text(size = rel(1), angle = 0),
axis.text.y = element_text(size = rel(1)))
p2 <- VlnPlot(seurat_RNA,
features = c("NFKB1"),
group.by = "merged", pt.size = 0) + NoLegend() + xlab("") + ylab("") +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_text(size = rel(1), angle = 0),
axis.text.y = element_text(size = rel(1)))
p3 <- VlnPlot(seurat_regulons,
features = c("NFKB1(+)"),
group.by = "merged", pt.size = 0) + NoLegend() + xlab("") + ylab("") +
theme(legend.position = "none",
axis.title.y = element_text(size = rel(1), angle = 0),
axis.text.y = element_text(size = rel(1)))
#pdf(paste(figures_folder, "violin_module_r-entry.pdf", sep=""),width=4,height=8,paper='special')
#patchwork::wrap_plots(plotlist = list(p1,p2,p3), ncol = 1)
p1
p2
p3
6 Session Info
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS/LAPACK: /Users/pauli/opt/anaconda3/envs/Motif_TF/lib/libopenblasp-r0.3.10.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] writexl_1.4.0 viridis_0.5.1 viridisLite_0.4.0 biomaRt_2.44.4 Homo.sapiens_1.3.1 TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 org.Hs.eg.db_3.11.4 GO.db_3.11.4 OrganismDbi_1.30.0 GenomicFeatures_1.40.1 GenomicRanges_1.40.0 GenomeInfoDb_1.24.2 AnnotationDbi_1.50.3 IRanges_2.22.1 S4Vectors_0.26.0 Biobase_2.48.0 BiocGenerics_0.34.0 GOfuncR_1.8.0 vioplot_0.3.7 zoo_1.8-9 sm_2.2-5.7 qlcMatrix_0.9.7 sparsesvd_0.2 slam_0.1-47 Matrix_1.3-4 UCell_1.2.0 plyr_1.8.6 ggpubr_0.4.0 reshape2_1.4.4
## [30] pheatmap_1.0.12 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.5 tidyverse_1.3.0 harmony_1.0 Rcpp_1.0.6 Signac_1.2.1 SeuratWrappers_0.3.0 SeuratObject_4.0.2 Seurat_4.0.3 BiocStyle_2.16.1
##
## loaded via a namespace (and not attached):
## [1] rappdirs_0.3.3 SnowballC_0.7.0 rtracklayer_1.48.0 scattermore_0.7 bit64_4.0.5 knitr_1.30 irlba_2.3.3 DelayedArray_0.14.0 data.table_1.14.0 rpart_4.1-15 RCurl_1.98-1.2 generics_0.1.0 cowplot_1.1.1 RSQLite_2.2.1 RANN_2.6.1 future_1.21.0 bit_4.0.4 spatstat.data_2.1-0 xml2_1.3.2 lubridate_1.7.9 httpuv_1.6.1 SummarizedExperiment_1.18.1 assertthat_0.2.1 xfun_0.18 hms_0.5.3 evaluate_0.14 promises_1.2.0.1 fansi_0.5.0 progress_1.2.2 dbplyr_1.4.4 readxl_1.3.1 igraph_1.2.6 DBI_1.1.0 htmlwidgets_1.5.3 spatstat.geom_2.2-0 ellipsis_0.3.2 crosstalk_1.1.1 backports_1.1.10 bookdown_0.21 deldir_0.2-10 vctrs_0.3.8 here_1.0.1
## [43] remotes_2.4.1 ROCR_1.0-11 abind_1.4-5 withr_2.4.2 ggforce_0.3.2 sctransform_0.3.2 GenomicAlignments_1.24.0 prettyunits_1.1.1 goftest_1.2-2 cluster_2.1.0 lazyeval_0.2.2 crayon_1.4.1 labeling_0.4.2 pkgconfig_2.0.3 tweenr_1.0.1 nlme_3.1-150 rlang_0.4.11 globals_0.14.0 lifecycle_1.0.0 miniUI_0.1.1.1 BiocFileCache_1.12.1 modelr_0.1.8 rsvd_1.0.3 rprojroot_2.0.2 cellranger_1.1.0 polyclip_1.10-0 matrixStats_0.59.0 lmtest_0.9-38 graph_1.66.0 ggseqlogo_0.1 carData_3.0-4 reprex_0.3.0 ggridges_0.5.3 png_0.1-7 bitops_1.0-7 KernSmooth_2.23-17 Biostrings_2.56.0 blob_1.2.1 parallelly_1.26.1 mapplots_1.5.1 rstatix_0.6.0 ggsignif_0.6.0
## [85] scales_1.1.1 memoise_1.1.0 magrittr_2.0.1 ica_1.0-2 zlibbioc_1.34.0 compiler_4.0.3 RColorBrewer_1.1-2 fitdistrplus_1.1-5 Rsamtools_2.4.0 cli_3.0.0 XVector_0.28.0 listenv_0.8.0 patchwork_1.1.1 pbapply_1.4-3 MASS_7.3-53 mgcv_1.8-33 tidyselect_1.1.1 stringi_1.6.2 yaml_2.2.1 askpass_1.1 ggrepel_0.9.1 fastmatch_1.1-0 tools_4.0.3 future.apply_1.7.0 rio_0.5.16 rstudioapi_0.11 foreign_0.8-80 lsa_0.73.2 gridExtra_2.3 farver_2.1.0 Rtsne_0.15 digest_0.6.27 BiocManager_1.30.10 shiny_1.6.0 car_3.0-10 broom_0.7.2 later_1.2.0 RcppAnnoy_0.0.18 httr_1.4.2 colorspace_2.0-2 rvest_0.3.6 XML_3.99-0.3
## [127] fs_1.5.0 tensor_1.5 reticulate_1.20 splines_4.0.3 uwot_0.1.10 RBGL_1.64.0 RcppRoll_0.3.0 spatstat.utils_2.2-0 plotly_4.9.4.1 xtable_1.8-4 jsonlite_1.7.2 R6_2.5.0 pillar_1.6.1 htmltools_0.5.1.1 mime_0.11 DT_0.16 glue_1.4.2 fastmap_1.1.0 BiocParallel_1.22.0 codetools_0.2-17 utf8_1.2.1 lattice_0.20-41 spatstat.sparse_2.0-0 curl_4.3.2 leiden_0.3.8 gtools_3.9.2 zip_2.1.1 openxlsx_4.2.4 openssl_1.4.4 survival_3.2-7 rmarkdown_2.5 docopt_0.7.1 munsell_0.5.0 GenomeInfoDbData_1.2.3 haven_2.3.1 gtable_0.3.0 spatstat.core_2.2-0